• PARTHA PAKRAY

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    • Scientific Text Entailment and a Textual-Entailment-based framework for cooking domain question answering

      AMARNATH PATHAK RIYANKA MANNA PARTHA PAKRAY DIPANKAR DAS ALEXANDER GELBUKH SIVAJI BANDYOPADHYAY

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      Detecting entailment relationship between two sentences has profoundly impacted several different application areas of Natural Language Processing (NLP). Though recognizing textual entailment (TE) is amongst the widely studied problems, the research on detecting entailment between pieces of scientific texts is still in its infancy. To this end the paper discusses implementation of systems based on Long Short-Term Memory (LSTM) neural network and Support Vector Machine (SVM) classifiers using SCITAIL entailment dataset, a dataset in which premise and hypothesis are constituted of scientific texts. Also, a TE-based framework for cooking domain question answering is introduced. The proposed framework exploits the entailment relationship between user question and the cooking questions contained inside a Knowledge Base (KB).

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